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Evidence networks for the analysis of biological systems

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Title: Evidence networks for the analysis of biological systems


1
Evidence networks for the analysis of biological
systems
  • Rainer Breitling
  • IBLS Molecular Plant Science group
  • Bioinformatics Research Centre
  • University of Glasgow, Scotland, UK

2
Background
Datasets and evidence networks in post-genomic
biology
3
Genomics
Fully sequenced genomes (1995-2004) 18
archaea 163 bacteria 3 protozoa 24 yeast species
and fungi 2 plants (Arabidopsis, rice) 2 insects
(flies, honey bee) 2 worms (C.elegans, C.
briggsae) 3 fish (fugu, puffer,
zebrafish) chicken, cow, dog, mouse, rat,
chimp human ? lots of lists of genes
4
Transcriptomics
  • microarrays measure gene expression levels (mRNA
    concentrations)
  • relative or absolute values
  • in organisms, tissues, cells
  • produce gene lists (e.g., which genes are
    up-regulated by a disease, by drug treatment, in
    a certain tissue)

5
Proteomics
  • 2D gels, liquid chromatography, and mass
    spectrometry measure protein concentrations
  • in tissues, cells, organelles
  • detect chemical modifications and processing of
    proteins
  • produces lists of protein variants that are
    different among conditions

6
Metabolomics
  • chromatography and mass spectrometry measure
    metabolite concentrations
  • in tissues, cells, body fluids, cell culture
    medium
  • produces lists of affected metabolites

7
Evidence networks
  • relate items (genes, proteins, metabolites) that
    have something to do with each other
  • relationship is based on objective evidence
  • represented as bipartite graphs
  • two classes of nodes items and evidence
  • automated analysis of results possible
  • intuitive visualization and links to literature

8
Types of evidence networks
  • Relationship can be based on
  • physical neighborhood
  • phyletic pattern similarity
  • expressional correlation
  • biophysical similarity
  • chemical transformation
  • functional co-operation
  • literature co-citations

9
Types of evidence networks
  • Relationship can be based on
  • physical neighborhood
  • phyletic pattern similarity
  • expressional correlation
  • biophysical similarity
  • chemical transformation
  • functional co-operation
  • literature co-citations

A O M P K Z Y Q V D R L B C E F G H S N U J
X I T W phy a o m p k z y - - d - l - - - - - -
- - - - - i t 22 aompkzy--d-l-----------it-
NtpA C H-ATPase subunit A 17
aompkzy--d-l-----------it- NtpB C H-ATPase
subunit B 17 aompkzy--d-l-----------it- NtpD C
H-ATPase subunit D 18 aompkzy--d-l-----------it-
NtpI C H-ATPase subunit I
10
Types of evidence networks
  • Relationship can be based on
  • physical neighborhood
  • phyletic pattern similarity
  • expressional correlation
  • biophysical similarity
  • chemical transformation
  • functional co-operation
  • literature co-citations

11
Types of evidence networks
  • Relationship can be based on
  • physical neighborhood
  • phyletic pattern similarity
  • expressional correlation
  • biophysical similarity
  • chemical transformation
  • functional co-operation
  • literature co-citations

12
Types of evidence networks
  • Relationship can be based on
  • physical neighborhood
  • phyletic pattern similarity
  • expressional correlation
  • biophysical similarity
  • chemical transformation
  • functional co-operation
  • literature co-citations

13
Types of evidence networks
  • Relationship can be based on
  • physical neighborhood
  • phyletic pattern similarity
  • expressional correlation
  • biophysical similarity
  • chemical transformation
  • functional co-operation
  • literature co-citations

14
Types of evidence networks
  • Relationship can be based on
  • physical neighborhood
  • phyletic pattern similarity
  • expressional correlation
  • biophysical similarity
  • chemical transformation
  • functional co-operation
  • literature co-citations

15
What is the big picture? Graph-based iterative
Group Analysis for the automated interpretation
of biological datasets lists graphs
understanding
16
What does this list mean?
  Fold-Change Gene Symbol Gene Title
1 26.45 TNFAIP6 tumor necrosis factor, alpha-induced protein 6
2 25.79 THBS1 thrombospondin 1
3 23.08 SERPINE2 serine (or cysteine) proteinase inhibitor, clade E (nexin, plasminogen activator inhibitor type 1), member 2
4 21.5 PTX3 pentaxin-related gene, rapidly induced by IL-1 beta
5 18.82 THBS1 thrombospondin 1
6 16.68 CXCL10 chemokine (C-X-C motif) ligand 10
7 18.23 CCL4 chemokine (C-C motif) ligand 4
8 14.85 SOD2 superoxide dismutase 2, mitochondrial
9 13.62 IL1B interleukin 1, beta
10 11.53 CCL20 chemokine (C-C motif) ligand 20
11 11.82 CCL3 chemokine (C-C motif) ligand 3
12 11.27 SOD2 superoxide dismutase 2, mitochondrial
13 10.89 GCH1 GTP cyclohydrolase 1 (dopa-responsive dystonia)
14 10.73 IL8 interleukin 8
15 9.98 ICAM1 intercellular adhesion molecule 1 (CD54), human rhinovirus receptor
16 9.97 SLC2A6 solute carrier family 2 (facilitated glucose transporter), member 6
17 8.36 BCL2A1 BCL2-related protein A1
18 7.33 TNFAIP2 tumor necrosis factor, alpha-induced protein 2
19 6.97 SERPINB2 serine (or cysteine) proteinase inhibitor, clade B (ovalbumin), member 2
20 6.69 MAFB v-maf musculoaponeurotic fibrosarcoma oncogene homolog B (avian)
17
iterative Group Analysis (iGA)
iGA uses simple hypergeometric distribution to
obtain p-values Breitling et al., BMC
Bioinformatics, 2004, 534
18
Graph-based iGA
Breitling et al., BMC Bioinformatics, 2004, 5100
19
Graph-based iGA
1. step build the network
Breitling et al., BMC Bioinformatics, 2004, 5100
20
Graph-based iGA
2. step assign ranks to genes
Breitling et al., BMC Bioinformatics, 2004, 5100
21
Graph-based iGA
3. step find local minima
p 1/8 0.125
p 6/8 0.75
p 2/8 0.25
Breitling et al., BMC Bioinformatics, 2004, 5100
22
Graph-based iGA
4. step extend subgraph from minima
p0.014
p0.018
p1
p0.125
Breitling et al., BMC Bioinformatics, 2004, 5100
23
Graph-based iGA
5. step select p-value minimum
p0.018
p0.014
p1
p0.125
Breitling et al., BMC Bioinformatics, 2004, 5100
24
Advantages of GiGA
  • fast, unbiased and comprehensive analysis
  • assignment of statistical significance values to
    interpretation
  • detection of significant changes even if data are
    too noisy to reliably detect changed genes
  • statistically meaningful interpretation already
    without replication experiments
  • detection of patterns even for small absolute
    changes
  • flexible use of annotations intuitive
    visualization

25
Example 1
Microarrays Gene expression changes during the
yeast diauxic shift
26
Yeast diauxic shift studyDeRisi et al.
(1997)Science 278 680-6
27
Yeast diauxic shift study
  0h 9.5h 11.5h 13.5h 15.5h 18.5h 20.5h
UP     6144 - purine base metabolism 6099 - tricarboxylic acid cycle 6099 - tricarboxylic acid cycle 3773 - heat shock protein activity 6099 - tricarboxylic acid cycle
      9277 - cell wall (sensu Fungi) 3773 - heat shock protein activity 5749 - respiratory chain complex II (sensu Eukarya) 6099 - tricarboxylic acid cycle 3773 - heat shock protein activity
      297 - spermine transporter activity 6950 - response to stress 6121 - oxidative phosphorylation, succinate to ubiquinone 5977 - glycogen metabolism 5749 - respiratory chain complex II (sensu Eukarya)
      15846 - polyamine transport 297 - spermine transporter activity 8177 - succinate dehydrogenase (ubiquinone) activity 6950 - response to stress 6121 - oxidative phosphorylation, succinate to ubiquinone
        4373 - glycogen (starch) synthase activity 3773 - heat shock protein activity 4373 - glycogen (starch) synthase activity 8177 - succinate dehydrogenase (ubiquinone) activity
        15846 - polyamine transport 4373 - glycogen (starch) synthase activity 4129 - cytochrome c oxidase activity 6537 - glutamate biosynthesis
        5353 - fructose transporter activity 7039 - vacuolar protein catabolism 5751 - respiratory chain complex IV (sensu Eukarya) 6097 - glyoxylate cycle
        15578 - mannose transporter activity 6950 - response to stress 5749 - respiratory chain complex II (sensu Eukarya) 5750 - respiratory chain complex III (sensu Eukarya)
        7039 - vacuolar protein catabolism 4129 - cytochrome c oxidase activity 6121 - oxidative phosphorylation, succinate to ubiquinone 9060 - aerobic respiration
        8645 - hexose transport 5751 - respiratory chain complex IV (sensu Eukarya) 8177 - succinate dehydrogenase (ubiquinone) activity 4129 - cytochrome c oxidase activity
28
GiGA results diauxic shift
Down-regulated genes using GeneOntology-based network Down-regulated genes using GeneOntology-based network Down-regulated genes using GeneOntology-based network Down-regulated genes using GeneOntology-based network Down-regulated genes using GeneOntology-based network
locus gene description ("anchor gene") p-value members max. rank
YHL015W ribosomal protein S20 5.87E-86 39 48
YMR217W GMP synthase 3.38E-13 9 172
YDR144C aspartyl proteaserelated to Yap3p 4.06E-08 6 242
YNL065W multidrug resistance transporter 4.02E-05 3 141
YLR062C 6.41E-05 4 367
YGL225W May regulate Golgi function and glycosylation in Golgi 1.12E-04 4 422
YPR074C transketolase 1 1.44E-04 4 449
total genes measured in network 4087. total genes measured in network 4087.
29
small ribosomal subunit
large ribosomal subunit
nucleolar rRNA processing
translational elongation
30
GiGA case study diauxic shift
Up-regulated genes using metabolic network Up-regulated genes using metabolic network Up-regulated genes using metabolic network Up-regulated genes using metabolic network
locus gene description p-value members max. rank
YER065C isocitrate lyase 4.96E-53 39 54
YGR088W catalase T 3.09E-10 11 106
YFR015C glycogen synthase (UDP-glucose-starch glucosyltransferase) 2.08E-04 3 45
YJR073C unsaturated phospholipid N-methyltransferase 3.85E-04 5 156
YDR001C neutral trehalase 5.01E-04 3 60
YCR014C DNA polymerase IV 5.44E-04 17 481
YIR038C glutathione transferase 8.64E-04 5 183
total genes measured in network 744. total genes measured in network 744.
31
respiratory chain complex II
glyoxylate cycle
citrate (TCA) cycle
oxidative phosphorylation (complex V)
respiratory chain complex III
32
respiratory chain complex IV
33
Example 2
Metabolomics Changes in metabolic profiles in
drug-treated trypanosomes
34
GiGA applied to metabolomics data
  • Challenge No annotation available
  • Solution Build evidence network based on
    hypothetical reactions between observed masses
    (mass differences)

35
Metabolite tree of mass 257.1028
(glycerylphosphorylcholine)
6 generations
36
Metabolite tree of mass 257.1028
4 generations
37
Metabolite tree of mass 257.1028
2 generations
38
Metabolite tree of mass 257.1028
colors indicate changes of metabolite signals
compared to untreated samples after 60 min
pentamidine (red down, green up)
39
GiGA metabolite trees for one experimental example
40
Choline tree found by GiGA(most significant
subgraph, plt10-13)
extracted from
41
Summary
  • post-genomic technologies produces lists
  • neighborhood relationships yield evidence
    networks (graphs)
  • lists graphs biological insights
  • GiGA graph analysis highlights and connects
    relevant areas in the evidence network

42
Acknowledgements
  • Pawel Herzyk Sir Henry Wellcome Functional
    Genomics Facility
  • Anna Amtmann Patrick Armengaud IBLS Molecular
    Plant Science group
  • Mike Barrett IBLS Parasitology Research group
  • FGF academic users Wilhelmina Behan, Simone
    Boldt, Anna Casburn-Jones, Gillian Douce, Paul
    Everest, Michael Farthing, Heather Johnston,
    Walter Kolch, Peter O'Shaughnessy, Susan Pyne,
    Rosemary Smith, Hawys Williams

43
Contact
  • Rainer Breitling
  • Bioinformatics Research Centre
  • Davidson Building A416
  • University of Glasgow, Scotland, UK
  • R.Breitling_at_bio.gla.ac.uk
  • http//www.brc.dcs.gla.ac.uk/rb106x
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